{"ID":2888559,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00172","arxiv_id":"2508.00172","title":"DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission","abstract":"The rapid development of artificial intelligence has driven smart health with next-generation wireless communication technologies, stimulating exciting applications in remote diagnosis and intervention. To enable a timely and effective response for remote healthcare, efficient transmission of medical data through noisy channels with limited bandwidth emerges as a critical challenge. In this work, we propose a novel diffusion-based semantic communication framework, namely DiSC-Med, for the medical image transmission, where medical-enhanced compression and denoising blocks are developed for bandwidth efficiency and robustness, respectively. Unlike conventional pixel-wise communication framework, our proposed DiSC-Med is able to capture the key semantic information and achieve superior reconstruction performance with ultra-high bandwidth efficiency against noisy channels. Extensive experiments on real-world medical datasets validate the effectiveness of our framework, demonstrating its potential for robust and efficient telehealth applications.","short_abstract":"The rapid development of artificial intelligence has driven smart health with next-generation wireless communication technologies, stimulating exciting applications in remote diagnosis and intervention. To enable a timely and effective response for remote healthcare, efficient transmission of medical data through noisy...","url_abs":"https://arxiv.org/abs/2508.00172","url_pdf":"https://arxiv.org/pdf/2508.00172v1","authors":"[\"Fupei Guo\",\"Hao Zheng\",\"Xiang Zhang\",\"Li Chen\",\"Yue Wang\",\"Songyang Zhang\"]","published":"2025-07-31T21:36:45Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.IV\"]","methods":"[\"Diffusion Model\"]","has_code":false}